AI In Advance Driver Assistance System

Authors

  • K. S. Dhruva Teja  Department of AI and ML, New Horizon College of Engineering, Bengaluru, Karnataka, India
  • P. Saketh Sree Ram  Department of AI and ML, New Horizon College of Engineering, Bengaluru, Karnataka, India
  • K. Vamshivardhan  Department of AI and ML, New Horizon College of Engineering, Bengaluru, Karnataka, India
  • R. Roopam Chowdhury  Department of AI and ML, New Horizon College of Engineering, Bengaluru, Karnataka, India

DOI:

https://doi.org//10.32628/IJSRST229662

Keywords:

Advanced Driver Assistance Systems, High-level Driver

Abstract

Perhaps of the most encouraging sub-capability in the field of savvy traffic frameworks is the High-level Driver Help Framework which is termed as ADAS. Many high-level wellbeing highlights are accessible in new vehicles. Airbags, safety belts and any remaining essential aloof wellbeing highlights are standard. Vehicles are currently frequently furnished with cutting edge dynamic wellbeing frameworks that can forestall mishaps. The conceivable outcomes of cutting-edge help frameworks are continually growing. People assume a significant part in this cycle and are additionally the most vulnerable connection as 90% of mishaps are brought about by human mistake and lack of regard. Various mishaps are accounted for each year because of unnecessary speed and unfortunate driving choices. The vast majority of these can now be tried not to by use the wellbeing highlights remembered for cutting edge driver help frameworks.

References

  1. Aleksandra Simic, Ognjen Kocic, Milan Z. Bjelica and Milena Milosevic “Driver monitoring algorithm for Advanced Driver Assistance Systems", 24th Telecommunications Forum, IEEE,2017.
  2. Seyed Mehdi Iranmanesh, Hossein Nourkhiz Mahjoub, Hadi Kazemi, and Yaser P. Fallah “An Adaptive Forward Collision Warning Framework Design Based on Driver Distraction”, IEEE Transactions on Intelligent Transportation Systems, IEEE, 2018.
  3. Chang Wang, Qinyu Sun, Yingshi Guo, Rui Fu, and Wei Yuan “Improving the User Acceptability of Advanced Driver Assistance Systems Based on Different Driving Styles: A Case Study of Lane Change Warning Systems”, IEEE Transactions on Intelligent Transportation Systems, IEEE, 2019.
  4. Martina Hasenjager, Martin Heckmann, and Heiko Wersing “A Survey of Personalization for Advanced Driver Assistance Systems”, IEEE Transactions on Intelligent Vehicles, IEEE, 2019.
  5. Keji Chen, Takuma Yamaguchi, Hiroyuki Okuda, Tatsuys Suzuki and Xuexun Guo “Realization and Evaluation of an Instructor-Like Assistance System for Collision Avoidance”, IEEE Transactions on Intelligent TransportationSystems”, IEEE Transactions on Intelligent Transportation Systems, IEEE, 2020.
  6. Jianqiang Wang; Chenfei Yu; Shengbo Eben Li and Likun Wang “A Forward Collision Warning Algorithm With Adaptation to Driver Behaviors”, IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Intelligent Transportation Systems, IEEE, 2015.
  7. Antonio Prioletti, Andreas Møgelmose, Paolo Grisleri, Mohan Manubhai Trivedi, Alberto Broggi and Thomas B. Moeslund “Part-Based Pedestrian Detection and Feature-Based Tracking for Driver Assistance: Real-Time, Robust Algorithms, and Evaluation”, IEEE Transactions on Intelligent Transportation Systems, IEEE, 2013.

Downloads

Published

2022-12-30

Issue

Section

Research Articles

How to Cite

[1]
K. S. Dhruva Teja, P. Saketh Sree Ram, K. Vamshivardhan, R. Roopam Chowdhury, " AI In Advance Driver Assistance System, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 9, Issue 6, pp.417-423, November-December-2022. Available at doi : https://doi.org/10.32628/IJSRST229662